Maize Disease Detection using Deep Learning Models

Maize Disease Detection using Deep Learning Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Maize Disease Detection using Deep Learning Models Wendy Mapamela, Boikanyo Radiokan, Stephen E. Moore, Abiodun Modupe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5763601/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Disease attacks on crops like maize pose a significant threat to the global food supply chain in Africa, particularly in West Africa. Maize is a staple food source and the economic backbone of the population and farmers in West Africa. In recent years, maize yields have declined due to diseases. Systematic solutions, such as visual inspection through laboratory experiments for disease diagnosis, have not led to a significant improvement in production or ensured sustainable food security in Africa. In response to this challenge, we introduce a lightweight deep-learning ensemble model for early disease detection in maize plants. The study focuses on developing and validating a model specifically designed to identify and classify diseases in maize plants. We use computer vision technology to capture intricate patterns in maize leaf images. The model is trained to recognise six classes, with five representing different diseases and one representing a healthy state. In this paper, we explore the amalgamation of Residual Network (ResNet9) and Efficient-Net-b4 (ENetb4) as a pre-training model built on convolutional neural networks (CNN) to improve accuracy and robustness in maize disease detection and prediction. The results of the study indicate significant opportunities for improvement in agricultural technology in West Africa. For instance, the ResNet9 model accurately identified diseased images of maize crops with a performance accuracy of 98.2%, while the (ENetb4) model achieved a performance accuracy of 94.3% in the same task. Artificial Intelligence and Machine Learning Maize Diseases Image Processing Deep learning model Convolution Neural Network Efficient-Net-b4 Res-Net-9 Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5763601","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397639131,"identity":"bbd5da0c-fbc1-4bc7-9f73-c5f34d1a6200","order_by":0,"name":"Wendy Mapamela","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3OsWrDMBCA4QuG83Ikq0RL/QpyAsWlhg59EQeB8woZBQF5yQPEb9G+gYXAkx8gnVrjIWMyZvBQh2whCHfroH+55T7uAHy+/1hYmK4XKWGoYKJGEWqkoHX+OCU7lrDsmVFj0ycmRxJRZTnj2hLyY813PUQzCNuTmxibxHpF+CCRf2qISwWSOYlRy/1Sv15JqyATFVRuYkEwo4PhMYv8p7+QyebsJDXMuWreCVmA/AMvJEDnFd6QXMA6JxzmS6lZXG4QExeZfh9MByJ9iwrTfm37NJqFRbd3kduGl4K/7Pt8Pp/vbr98nkOodHXb/wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Computer Science, University of Pretoria, South Africa","correspondingAuthor":true,"prefix":"","firstName":"Wendy","middleName":"","lastName":"Mapamela","suffix":""},{"id":397639132,"identity":"e770404c-cc72-445b-ad3b-a2061016a54d","order_by":1,"name":"Boikanyo Radiokan","email":"","orcid":"","institution":"Department of Computer Science, University of Pretoria, South Africa","correspondingAuthor":false,"prefix":"","firstName":"Boikanyo","middleName":"","lastName":"Radiokan","suffix":""},{"id":397639133,"identity":"3645b05b-8afb-4e2b-b084-4df3ba40a1e9","order_by":2,"name":"Stephen E. 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Maize is a staple food source and the economic backbone of the population and farmers in West Africa. In recent years, maize yields have declined due to diseases. Systematic solutions, such as visual inspection through laboratory experiments for disease diagnosis, have not led to a significant improvement in production or ensured sustainable food security in Africa. In response to this challenge, we introduce a lightweight deep-learning ensemble model for early disease detection in maize plants. The study focuses on developing and validating a model specifically designed to identify and classify diseases in maize plants. We use computer vision technology to capture intricate patterns in maize leaf images. The model is trained to recognise six classes, with five representing different diseases and one representing a healthy state. 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